Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations224
Missing cells150
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.3 KiB
Average record size in memory371.6 B

Variable types

Numeric15
Categorical12
DateTime1

Alerts

Contact_Cost has constant value "3" Constant
Total_Revenue has constant value "11" Constant
Annual_Income is highly overall correlated with Catalog_Orders and 9 other fieldsHigh correlation
Campaign_3 is highly overall correlated with Campaign_4 and 3 other fieldsHigh correlation
Campaign_4 is highly overall correlated with Campaign_3 and 1 other fieldsHigh correlation
Campaign_5 is highly overall correlated with Campaign_3High correlation
Catalog_Orders is highly overall correlated with Annual_Income and 9 other fieldsHigh correlation
Spent_Fish is highly overall correlated with Annual_Income and 8 other fieldsHigh correlation
Spent_Fruits is highly overall correlated with Annual_Income and 7 other fieldsHigh correlation
Spent_Gold is highly overall correlated with Annual_Income and 8 other fieldsHigh correlation
Spent_Meat is highly overall correlated with Annual_Income and 9 other fieldsHigh correlation
Spent_Sweets is highly overall correlated with Annual_Income and 7 other fieldsHigh correlation
Spent_Wines is highly overall correlated with Annual_Income and 9 other fieldsHigh correlation
Store_Orders is highly overall correlated with Annual_Income and 8 other fieldsHigh correlation
Web_Orders is highly overall correlated with Annual_Income and 5 other fieldsHigh correlation
Web_Visits is highly overall correlated with Annual_Income and 3 other fieldsHigh correlation
Campaign_1 is highly imbalanced (58.1%) Imbalance
Campaign_2 is highly imbalanced (61.2%) Imbalance
Campaign_3 is highly imbalanced (55.3%) Imbalance
Campaign_4 is highly imbalanced (64.6%) Imbalance
Campaign_5 is highly imbalanced (89.7%) Imbalance
Complaint_Flag is highly imbalanced (92.6%) Imbalance
Campaign_3 has 149 (66.5%) missing values Missing
User_Key has unique values Unique
Last_Visit has 6 (2.7%) zeros Zeros
Spent_Fruits has 39 (17.4%) zeros Zeros
Spent_Fish has 37 (16.5%) zeros Zeros
Spent_Sweets has 43 (19.2%) zeros Zeros
Spent_Gold has 5 (2.2%) zeros Zeros
Promo_Purchases has 3 (1.3%) zeros Zeros
Catalog_Orders has 62 (27.7%) zeros Zeros
Web_Visits has 3 (1.3%) zeros Zeros

Reproduction

Analysis started2025-07-25 16:31:54.016812
Analysis finished2025-07-25 16:32:41.969709
Duration47.95 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

User_Key
Real number (ℝ)

Unique 

Distinct224
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5995.3527
Minimum153
Maximum11176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:42.031366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum153
5-th percentile869.3
Q13510.75
median5531
Q38804
95-th percentile10764.9
Maximum11176
Range11023
Interquartile range (IQR)5293.25

Descriptive statistics

Standard deviation3167.8367
Coefficient of variation (CV)0.52838204
Kurtosis-1.2055619
Mean5995.3527
Median Absolute Deviation (MAD)2750.5
Skewness0.049749108
Sum1342959
Variance10035189
MonotonicityNot monotonic
2025-07-25T21:32:42.135836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9370 1
 
0.4%
4682 1
 
0.4%
4530 1
 
0.4%
8212 1
 
0.4%
6409 1
 
0.4%
9058 1
 
0.4%
4299 1
 
0.4%
10413 1
 
0.4%
1890 1
 
0.4%
8414 1
 
0.4%
Other values (214) 214
95.5%
ValueCountFrequency (%)
153 1
0.4%
199 1
0.4%
359 1
0.4%
550 1
0.4%
569 1
0.4%
574 1
0.4%
615 1
0.4%
675 1
0.4%
679 1
0.4%
810 1
0.4%
ValueCountFrequency (%)
11176 1
0.4%
11114 1
0.4%
11013 1
0.4%
10995 1
0.4%
10968 1
0.4%
10965 1
0.4%
10906 1
0.4%
10888 1
0.4%
10837 1
0.4%
10821 1
0.4%

Birth_Year
Real number (ℝ)

Distinct48
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.5982
Minimum1943
Maximum1995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:42.227190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1943
5-th percentile1950
Q11958
median1970
Q31977
95-th percentile1988
Maximum1995
Range52
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.868835
Coefficient of variation (CV)0.0060290792
Kurtosis-0.76773871
Mean1968.5982
Median Absolute Deviation (MAD)9
Skewness-0.055544381
Sum440966
Variance140.86923
MonotonicityNot monotonic
2025-07-25T21:32:42.318900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1971 17
 
7.6%
1969 10
 
4.5%
1974 8
 
3.6%
1958 8
 
3.6%
1961 8
 
3.6%
1970 8
 
3.6%
1955 7
 
3.1%
1973 7
 
3.1%
1956 7
 
3.1%
1976 7
 
3.1%
Other values (38) 137
61.2%
ValueCountFrequency (%)
1943 1
 
0.4%
1945 2
 
0.9%
1946 2
 
0.9%
1948 4
1.8%
1949 2
 
0.9%
1950 7
3.1%
1951 2
 
0.9%
1952 6
2.7%
1953 2
 
0.9%
1954 3
1.3%
ValueCountFrequency (%)
1995 2
 
0.9%
1993 1
 
0.4%
1991 2
 
0.9%
1990 2
 
0.9%
1989 2
 
0.9%
1988 4
1.8%
1987 4
1.8%
1985 2
 
0.9%
1984 5
2.2%
1983 7
3.1%

Edu_Level
Categorical

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Graduation
115 
PhD
49 
Master
33 
2n Cycle
20 
Basic
 
7

Length

Max length10
Median length10
Mean length7.5446429
Min length3

Characters and Unicode

Total characters1690
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhD
2nd rowGraduation
3rd rowMaster
4th rowMaster
5th rowGraduation

Common Values

ValueCountFrequency (%)
Graduation 115
51.3%
PhD 49
21.9%
Master 33
 
14.7%
2n Cycle 20
 
8.9%
Basic 7
 
3.1%

Length

2025-07-25T21:32:42.415366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:42.476876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
graduation 115
47.1%
phd 49
20.1%
master 33
 
13.5%
2n 20
 
8.2%
cycle 20
 
8.2%
basic 7
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a 270
16.0%
r 148
8.8%
t 148
8.8%
n 135
 
8.0%
i 122
 
7.2%
G 115
 
6.8%
u 115
 
6.8%
d 115
 
6.8%
o 115
 
6.8%
e 53
 
3.1%
Other values (12) 354
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 270
16.0%
r 148
8.8%
t 148
8.8%
n 135
 
8.0%
i 122
 
7.2%
G 115
 
6.8%
u 115
 
6.8%
d 115
 
6.8%
o 115
 
6.8%
e 53
 
3.1%
Other values (12) 354
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 270
16.0%
r 148
8.8%
t 148
8.8%
n 135
 
8.0%
i 122
 
7.2%
G 115
 
6.8%
u 115
 
6.8%
d 115
 
6.8%
o 115
 
6.8%
e 53
 
3.1%
Other values (12) 354
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 270
16.0%
r 148
8.8%
t 148
8.8%
n 135
 
8.0%
i 122
 
7.2%
G 115
 
6.8%
u 115
 
6.8%
d 115
 
6.8%
o 115
 
6.8%
e 53
 
3.1%
Other values (12) 354
20.9%

Family_Status
Categorical

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
Married
84 
Together
61 
Single
53 
Divorced
22 
Widow
 
4

Length

Max length8
Median length7
Mean length7.0982143
Min length5

Characters and Unicode

Total characters1590
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowWidow
4th rowMarried
5th rowDivorced

Common Values

ValueCountFrequency (%)
Married 84
37.5%
Together 61
27.2%
Single 53
23.7%
Divorced 22
 
9.8%
Widow 4
 
1.8%

Length

2025-07-25T21:32:42.561326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:42.738936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 84
37.5%
together 61
27.2%
single 53
23.7%
divorced 22
 
9.8%
widow 4
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 281
17.7%
r 251
15.8%
i 163
10.3%
g 114
 
7.2%
d 110
 
6.9%
o 87
 
5.5%
M 84
 
5.3%
a 84
 
5.3%
T 61
 
3.8%
t 61
 
3.8%
Other values (9) 294
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 281
17.7%
r 251
15.8%
i 163
10.3%
g 114
 
7.2%
d 110
 
6.9%
o 87
 
5.5%
M 84
 
5.3%
a 84
 
5.3%
T 61
 
3.8%
t 61
 
3.8%
Other values (9) 294
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 281
17.7%
r 251
15.8%
i 163
10.3%
g 114
 
7.2%
d 110
 
6.9%
o 87
 
5.5%
M 84
 
5.3%
a 84
 
5.3%
T 61
 
3.8%
t 61
 
3.8%
Other values (9) 294
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 281
17.7%
r 251
15.8%
i 163
10.3%
g 114
 
7.2%
d 110
 
6.9%
o 87
 
5.5%
M 84
 
5.3%
a 84
 
5.3%
T 61
 
3.8%
t 61
 
3.8%
Other values (9) 294
18.5%

Annual_Income
Real number (ℝ)

High correlation 

Distinct223
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean50344.072
Minimum2447
Maximum105471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:42.839877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2447
5-th percentile19524.6
Q132443
median49160
Q367493
95-th percentile82778.2
Maximum105471
Range103024
Interquartile range (IQR)35050

Descriptive statistics

Standard deviation20969.987
Coefficient of variation (CV)0.4165334
Kurtosis-0.90960028
Mean50344.072
Median Absolute Deviation (MAD)17399
Skewness0.035935067
Sum11226728
Variance4.3974037 × 108
MonotonicityNot monotonic
2025-07-25T21:32:42.955356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65846 1
 
0.4%
51876 1
 
0.4%
78427 1
 
0.4%
39791 1
 
0.4%
57136 1
 
0.4%
79800 1
 
0.4%
70971 1
 
0.4%
72570 1
 
0.4%
42033 1
 
0.4%
33419 1
 
0.4%
Other values (213) 213
95.1%
ValueCountFrequency (%)
2447 1
0.4%
7144 1
0.4%
7500 1
0.4%
8820 1
0.4%
14045 1
0.4%
15056 1
0.4%
15345 1
0.4%
15716 1
0.4%
16626 1
0.4%
16927 1
0.4%
ValueCountFrequency (%)
105471 1
0.4%
91172 1
0.4%
91065 1
0.4%
90273 1
0.4%
87188 1
0.4%
87171 1
0.4%
86424 1
0.4%
85738 1
0.4%
83715 1
0.4%
83273 1
0.4%

Kids_Count
Categorical

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
0
123 
1
97 
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters224
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123
54.9%
1 97
43.3%
2 4
 
1.8%

Length

2025-07-25T21:32:43.053549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:43.106928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 123
54.9%
1 97
43.3%
2 4
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 123
54.9%
1 97
43.3%
2 4
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 123
54.9%
1 97
43.3%
2 4
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 123
54.9%
1 97
43.3%
2 4
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 123
54.9%
1 97
43.3%
2 4
 
1.8%

Teens_Count
Categorical

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
0
122 
1
97 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters224
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 122
54.5%
1 97
43.3%
2 5
 
2.2%

Length

2025-07-25T21:32:43.177118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:43.230119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 122
54.5%
1 97
43.3%
2 5
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 122
54.5%
1 97
43.3%
2 5
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 122
54.5%
1 97
43.3%
2 5
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 122
54.5%
1 97
43.3%
2 5
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 122
54.5%
1 97
43.3%
2 5
 
2.2%
Distinct194
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Minimum2012-01-10 00:00:00
Maximum2014-12-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T21:32:43.315393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:43.444961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Last_Visit
Real number (ℝ)

Zeros 

Distinct91
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.044643
Minimum0
Maximum99
Zeros6
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:43.569598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.15
Q121
median47.5
Q371.25
95-th percentile93.7
Maximum99
Range99
Interquartile range (IQR)50.25

Descriptive statistics

Standard deviation28.832869
Coefficient of variation (CV)0.61288315
Kurtosis-1.2136367
Mean47.044643
Median Absolute Deviation (MAD)25.5
Skewness0.10175824
Sum10538
Variance831.33432
MonotonicityNot monotonic
2025-07-25T21:32:43.673398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 6
 
2.7%
16 6
 
2.7%
50 6
 
2.7%
0 6
 
2.7%
92 5
 
2.2%
30 5
 
2.2%
29 5
 
2.2%
12 5
 
2.2%
76 4
 
1.8%
27 4
 
1.8%
Other values (81) 172
76.8%
ValueCountFrequency (%)
0 6
2.7%
1 2
 
0.9%
2 1
 
0.4%
3 2
 
0.9%
4 1
 
0.4%
5 2
 
0.9%
6 1
 
0.4%
7 1
 
0.4%
8 3
1.3%
9 2
 
0.9%
ValueCountFrequency (%)
99 1
 
0.4%
98 1
 
0.4%
97 3
1.3%
96 3
1.3%
95 1
 
0.4%
94 3
1.3%
92 5
2.2%
90 1
 
0.4%
89 2
 
0.9%
88 2
 
0.9%

Spent_Wines
Real number (ℝ)

High correlation 

Distinct160
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.31696
Minimum0
Maximum1349
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:43.771651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q122.25
median136.5
Q3407.25
95-th percentile1001
Maximum1349
Range1349
Interquartile range (IQR)385

Descriptive statistics

Standard deviation320.36427
Coefficient of variation (CV)1.1895436
Kurtosis1.1868407
Mean269.31696
Median Absolute Deviation (MAD)128.5
Skewness1.3961958
Sum60327
Variance102633.27
MonotonicityNot monotonic
2025-07-25T21:32:43.884747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 8
 
3.6%
11 4
 
1.8%
4 4
 
1.8%
8 4
 
1.8%
1 4
 
1.8%
23 4
 
1.8%
2 4
 
1.8%
14 4
 
1.8%
46 3
 
1.3%
13 3
 
1.3%
Other values (150) 182
81.2%
ValueCountFrequency (%)
0 1
 
0.4%
1 4
1.8%
2 4
1.8%
3 1
 
0.4%
4 4
1.8%
5 8
3.6%
6 2
 
0.9%
7 2
 
0.9%
8 4
1.8%
9 2
 
0.9%
ValueCountFrequency (%)
1349 1
0.4%
1308 1
0.4%
1239 1
0.4%
1193 1
0.4%
1099 1
0.4%
1090 1
0.4%
1060 1
0.4%
1043 1
0.4%
1039 1
0.4%
1009 1
0.4%

Spent_Fruits
Real number (ℝ)

High correlation  Zeros 

Distinct68
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.607143
Minimum0
Maximum181
Zeros39
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:43.992056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q330
95-th percentile122.55
Maximum181
Range181
Interquartile range (IQR)29

Descriptive statistics

Standard deviation39.542531
Coefficient of variation (CV)1.5441993
Kurtosis3.5571639
Mean25.607143
Median Absolute Deviation (MAD)8
Skewness2.0534096
Sum5736
Variance1563.6118
MonotonicityNot monotonic
2025-07-25T21:32:44.094259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39
17.4%
1 20
 
8.9%
4 15
 
6.7%
2 13
 
5.8%
3 13
 
5.8%
17 6
 
2.7%
6 6
 
2.7%
9 6
 
2.7%
11 5
 
2.2%
10 4
 
1.8%
Other values (58) 97
43.3%
ValueCountFrequency (%)
0 39
17.4%
1 20
8.9%
2 13
 
5.8%
3 13
 
5.8%
4 15
 
6.7%
5 3
 
1.3%
6 6
 
2.7%
7 2
 
0.9%
8 4
 
1.8%
9 6
 
2.7%
ValueCountFrequency (%)
181 1
0.4%
169 1
0.4%
161 1
0.4%
160 1
0.4%
152 1
0.4%
147 1
0.4%
140 1
0.4%
138 1
0.4%
131 1
0.4%
129 2
0.9%

Spent_Meat
Real number (ℝ)

High correlation 

Distinct137
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.55357
Minimum2
Maximum1725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:44.203053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q113
median59
Q3207
95-th percentile621.1
Maximum1725
Range1723
Interquartile range (IQR)194

Descriptive statistics

Standard deviation230.41123
Coefficient of variation (CV)1.4717724
Kurtosis10.232148
Mean156.55357
Median Absolute Deviation (MAD)49
Skewness2.6346487
Sum35068
Variance53089.333
MonotonicityNot monotonic
2025-07-25T21:32:44.306778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 7
 
3.1%
6 7
 
3.1%
3 6
 
2.7%
12 5
 
2.2%
2 5
 
2.2%
9 5
 
2.2%
13 5
 
2.2%
8 5
 
2.2%
10 5
 
2.2%
30 4
 
1.8%
Other values (127) 170
75.9%
ValueCountFrequency (%)
2 5
2.2%
3 6
2.7%
4 2
 
0.9%
5 2
 
0.9%
6 7
3.1%
7 4
1.8%
8 5
2.2%
9 5
2.2%
10 5
2.2%
11 7
3.1%
ValueCountFrequency (%)
1725 1
0.4%
898 1
0.4%
853 1
0.4%
818 2
0.9%
816 1
0.4%
813 1
0.4%
731 1
0.4%
724 1
0.4%
653 1
0.4%
650 1
0.4%

Spent_Fish
Real number (ℝ)

High correlation  Zeros 

Distinct80
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.09375
Minimum0
Maximum247
Zeros37
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:44.420056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q342.25
95-th percentile168
Maximum247
Range247
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation55.609829
Coefficient of variation (CV)1.4991698
Kurtosis3.2773231
Mean37.09375
Median Absolute Deviation (MAD)12
Skewness1.9841932
Sum8309
Variance3092.4531
MonotonicityNot monotonic
2025-07-25T21:32:44.646933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
16.5%
3 16
 
7.1%
2 15
 
6.7%
4 12
 
5.4%
6 10
 
4.5%
10 6
 
2.7%
20 5
 
2.2%
21 5
 
2.2%
8 5
 
2.2%
11 5
 
2.2%
Other values (70) 108
48.2%
ValueCountFrequency (%)
0 37
16.5%
1 1
 
0.4%
2 15
6.7%
3 16
7.1%
4 12
 
5.4%
5 1
 
0.4%
6 10
 
4.5%
7 3
 
1.3%
8 5
 
2.2%
10 6
 
2.7%
ValueCountFrequency (%)
247 1
0.4%
240 1
0.4%
227 1
0.4%
224 1
0.4%
220 1
0.4%
210 1
0.4%
202 1
0.4%
197 1
0.4%
184 1
0.4%
180 1
0.4%

Spent_Sweets
Real number (ℝ)

High correlation  Zeros 

Distinct72
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.696429
Minimum0
Maximum194
Zeros43
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:44.755610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q333.25
95-th percentile122.85
Maximum194
Range194
Interquartile range (IQR)32.25

Descriptive statistics

Standard deviation40.800914
Coefficient of variation (CV)1.5283286
Kurtosis2.9186682
Mean26.696429
Median Absolute Deviation (MAD)7
Skewness1.8868807
Sum5980
Variance1664.7146
MonotonicityNot monotonic
2025-07-25T21:32:44.862692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43
19.2%
1 21
 
9.4%
2 19
 
8.5%
4 9
 
4.0%
7 8
 
3.6%
13 8
 
3.6%
3 6
 
2.7%
6 6
 
2.7%
14 4
 
1.8%
12 4
 
1.8%
Other values (62) 96
42.9%
ValueCountFrequency (%)
0 43
19.2%
1 21
9.4%
2 19
8.5%
3 6
 
2.7%
4 9
 
4.0%
5 4
 
1.8%
6 6
 
2.7%
7 8
 
3.6%
8 3
 
1.3%
9 3
 
1.3%
ValueCountFrequency (%)
194 1
0.4%
176 1
0.4%
173 1
0.4%
152 1
0.4%
141 1
0.4%
137 2
0.9%
126 1
0.4%
125 1
0.4%
124 1
0.4%
123 2
0.9%

Spent_Gold
Real number (ℝ)

High correlation  Zeros 

Distinct96
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.415179
Minimum0
Maximum291
Zeros5
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:44.972287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18.75
median26
Q353.25
95-th percentile137.55
Maximum291
Range291
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation50.946309
Coefficient of variation (CV)1.2011339
Kurtosis5.7042896
Mean42.415179
Median Absolute Deviation (MAD)19
Skewness2.2334924
Sum9501
Variance2595.5264
MonotonicityNot monotonic
2025-07-25T21:32:45.067954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 14
 
6.2%
1 12
 
5.4%
7 8
 
3.6%
9 7
 
3.1%
2 6
 
2.7%
12 6
 
2.7%
0 5
 
2.2%
30 5
 
2.2%
13 4
 
1.8%
34 4
 
1.8%
Other values (86) 153
68.3%
ValueCountFrequency (%)
0 5
 
2.2%
1 12
5.4%
2 6
2.7%
3 3
 
1.3%
4 14
6.2%
5 3
 
1.3%
6 2
 
0.9%
7 8
3.6%
8 3
 
1.3%
9 7
3.1%
ValueCountFrequency (%)
291 1
0.4%
242 1
0.4%
241 1
0.4%
227 1
0.4%
224 2
0.9%
207 1
0.4%
192 1
0.4%
159 1
0.4%
157 1
0.4%
141 1
0.4%

Promo_Purchases
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2410714
Minimum0
Maximum15
Zeros3
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:45.154478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5.85
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.969478
Coefficient of variation (CV)0.87881091
Kurtosis11.88634
Mean2.2410714
Median Absolute Deviation (MAD)1
Skewness2.8615429
Sum502
Variance3.8788437
MonotonicityNot monotonic
2025-07-25T21:32:45.217336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 106
47.3%
2 51
22.8%
3 24
 
10.7%
4 17
 
7.6%
5 11
 
4.9%
6 5
 
2.2%
7 3
 
1.3%
0 3
 
1.3%
11 1
 
0.4%
12 1
 
0.4%
Other values (2) 2
 
0.9%
ValueCountFrequency (%)
0 3
 
1.3%
1 106
47.3%
2 51
22.8%
3 24
 
10.7%
4 17
 
7.6%
5 11
 
4.9%
6 5
 
2.2%
7 3
 
1.3%
10 1
 
0.4%
11 1
 
0.4%
ValueCountFrequency (%)
15 1
 
0.4%
12 1
 
0.4%
11 1
 
0.4%
10 1
 
0.4%
7 3
 
1.3%
6 5
 
2.2%
5 11
 
4.9%
4 17
 
7.6%
3 24
10.7%
2 51
22.8%

Web_Orders
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8616071
Minimum0
Maximum23
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:45.282651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum23
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7074965
Coefficient of variation (CV)0.70113204
Kurtosis10.101859
Mean3.8616071
Median Absolute Deviation (MAD)2
Skewness1.9916523
Sum865
Variance7.3305373
MonotonicityNot monotonic
2025-07-25T21:32:45.453386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 45
20.1%
3 41
18.3%
2 31
13.8%
4 29
12.9%
5 23
10.3%
7 21
9.4%
6 13
 
5.8%
8 12
 
5.4%
11 2
 
0.9%
0 2
 
0.9%
Other values (3) 5
 
2.2%
ValueCountFrequency (%)
0 2
 
0.9%
1 45
20.1%
2 31
13.8%
3 41
18.3%
4 29
12.9%
5 23
10.3%
6 13
 
5.8%
7 21
9.4%
8 12
 
5.4%
9 2
 
0.9%
ValueCountFrequency (%)
23 1
 
0.4%
11 2
 
0.9%
10 2
 
0.9%
9 2
 
0.9%
8 12
 
5.4%
7 21
9.4%
6 13
 
5.8%
5 23
10.3%
4 29
12.9%
3 41
18.3%

Catalog_Orders
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6696429
Minimum0
Maximum28
Zeros62
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:45.631618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile8
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2126108
Coefficient of variation (CV)1.203386
Kurtosis16.313248
Mean2.6696429
Median Absolute Deviation (MAD)1
Skewness2.7774483
Sum598
Variance10.320868
MonotonicityNot monotonic
2025-07-25T21:32:45.813260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 62
27.7%
1 52
23.2%
2 26
11.6%
6 17
 
7.6%
4 16
 
7.1%
3 14
 
6.2%
5 12
 
5.4%
8 8
 
3.6%
7 7
 
3.1%
9 7
 
3.1%
Other values (2) 3
 
1.3%
ValueCountFrequency (%)
0 62
27.7%
1 52
23.2%
2 26
11.6%
3 14
 
6.2%
4 16
 
7.1%
5 12
 
5.4%
6 17
 
7.6%
7 7
 
3.1%
8 8
 
3.6%
9 7
 
3.1%
ValueCountFrequency (%)
28 1
 
0.4%
11 2
 
0.9%
9 7
 
3.1%
8 8
 
3.6%
7 7
 
3.1%
6 17
7.6%
5 12
5.4%
4 16
7.1%
3 14
6.2%
2 26
11.6%

Store_Orders
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6116071
Minimum0
Maximum13
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:46.011047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q37
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3005026
Coefficient of variation (CV)0.58815639
Kurtosis-0.28712987
Mean5.6116071
Median Absolute Deviation (MAD)2
Skewness0.89284703
Sum1257
Variance10.893318
MonotonicityNot monotonic
2025-07-25T21:32:46.287956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 49
21.9%
4 36
16.1%
2 27
12.1%
6 23
10.3%
7 20
8.9%
5 16
 
7.1%
12 11
 
4.9%
13 11
 
4.9%
11 10
 
4.5%
9 8
 
3.6%
Other values (4) 13
 
5.8%
ValueCountFrequency (%)
0 1
 
0.4%
1 1
 
0.4%
2 27
12.1%
3 49
21.9%
4 36
16.1%
5 16
 
7.1%
6 23
10.3%
7 20
8.9%
8 7
 
3.1%
9 8
 
3.6%
ValueCountFrequency (%)
13 11
 
4.9%
12 11
 
4.9%
11 10
 
4.5%
10 4
 
1.8%
9 8
 
3.6%
8 7
 
3.1%
7 20
8.9%
6 23
10.3%
5 16
7.1%
4 36
16.1%

Web_Visits
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2767857
Minimum0
Maximum9
Zeros3
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2025-07-25T21:32:46.544560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8.85
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3263352
Coefficient of variation (CV)0.44086217
Kurtosis-0.91813107
Mean5.2767857
Median Absolute Deviation (MAD)2
Skewness-0.33095255
Sum1182
Variance5.4118354
MonotonicityNot monotonic
2025-07-25T21:32:46.690435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 43
19.2%
6 30
13.4%
8 30
13.4%
5 29
12.9%
3 25
11.2%
2 21
9.4%
4 20
8.9%
9 12
 
5.4%
1 11
 
4.9%
0 3
 
1.3%
ValueCountFrequency (%)
0 3
 
1.3%
1 11
 
4.9%
2 21
9.4%
3 25
11.2%
4 20
8.9%
5 29
12.9%
6 30
13.4%
7 43
19.2%
8 30
13.4%
9 12
 
5.4%
ValueCountFrequency (%)
9 12
 
5.4%
8 30
13.4%
7 43
19.2%
6 30
13.4%
5 29
12.9%
4 20
8.9%
3 25
11.2%
2 21
9.4%
1 11
 
4.9%
0 3
 
1.3%

Campaign_1
Categorical

Imbalance 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
0
205 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters224
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 205
91.5%
1 19
 
8.5%

Length

2025-07-25T21:32:46.889108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:47.031157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 205
91.5%
1 19
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 205
91.5%
1 19
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 205
91.5%
1 19
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 205
91.5%
1 19
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 205
91.5%
1 19
 
8.5%

Campaign_2
Categorical

Imbalance 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
0
207 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters224
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 207
92.4%
1 17
 
7.6%

Length

2025-07-25T21:32:47.193306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:47.326346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 207
92.4%
1 17
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 207
92.4%
1 17
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 207
92.4%
1 17
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 207
92.4%
1 17
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 207
92.4%
1 17
 
7.6%

Campaign_3
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)2.7%
Missing149
Missing (%)66.5%
Memory size12.1 KiB
0.0
68 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters225
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 68
30.4%
1.0 7
 
3.1%
(Missing) 149
66.5%

Length

2025-07-25T21:32:47.535998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:47.740906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 68
90.7%
1.0 7
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 143
63.6%
. 75
33.3%
1 7
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 143
63.6%
. 75
33.3%
1 7
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 143
63.6%
. 75
33.3%
1 7
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 143
63.6%
. 75
33.3%
1 7
 
3.1%

Campaign_4
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
0
209 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters224
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 209
93.3%
1 15
 
6.7%

Length

2025-07-25T21:32:47.972876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:48.116718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 209
93.3%
1 15
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 209
93.3%
1 15
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 209
93.3%
1 15
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 209
93.3%
1 15
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 209
93.3%
1 15
 
6.7%

Campaign_5
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
0
221 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters224
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 221
98.7%
1 3
 
1.3%

Length

2025-07-25T21:32:48.279437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:48.413179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 221
98.7%
1 3
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 221
98.7%
1 3
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 221
98.7%
1 3
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 221
98.7%
1 3
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 221
98.7%
1 3
 
1.3%

Complaint_Flag
Categorical

Imbalance 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
0
222 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters224
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 222
99.1%
1 2
 
0.9%

Length

2025-07-25T21:32:48.576709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:48.701508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 222
99.1%
1 2
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 222
99.1%
1 2
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 222
99.1%
1 2
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 222
99.1%
1 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 222
99.1%
1 2
 
0.9%

Contact_Cost
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
3
224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters224
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 224
100.0%

Length

2025-07-25T21:32:48.861769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:49.016071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 224
100.0%

Most occurring characters

ValueCountFrequency (%)
3 224
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 224
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 224
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 224
100.0%

Total_Revenue
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
11
224 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters448
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 224
100.0%

Length

2025-07-25T21:32:49.242524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:32:49.403306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11 224
100.0%

Most occurring characters

ValueCountFrequency (%)
1 448
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 448
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 448
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 448
100.0%

Interactions

2025-07-25T21:32:40.359415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:31:57.839767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:02.308867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:05.806249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:09.608966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:13.067420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:16.736166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:20.293156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:23.714625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:26.987205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:31.124930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:34.869480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.583997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.764891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.963937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.437360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:31:58.088837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:02.514923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:06.050127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:09.824747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:13.263340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:16.940241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:20.495559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:23.906091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:27.235095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:31.354877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:35.196061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.657457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.857947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.037485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.507936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:31:58.288781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:02.719567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:06.269528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:10.273979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:13.457641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:17.131776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:20.710104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:24.151172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:27.439951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:31.611571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:35.323235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.736789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.936475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.115880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.585900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:31:58.631134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:02.933270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:06.503136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:10.494308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:13.771689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:17.334830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:20.990312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:24.421775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:27.664814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:31.887653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:35.397731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.816349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.021306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.194187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.657765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:31:58.842691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:03.147645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:06.745599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:10.693928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:14.034006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:17.525628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:21.227696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:24.617286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:27.917831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:32.145105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:35.469108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.902894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.101819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.266189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.740313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:31:59.862792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:03.403977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:07.020137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:10.955817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:14.290855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:17.786631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:21.527880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:24.822774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:28.429531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:32.434180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:35.562728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.989909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.190358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.345398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.822957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:00.135933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:03.647495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:07.303353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:11.178742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:14.551461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:18.087977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:21.759178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:25.015039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:28.681611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:32.695127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:35.635641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.075516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.277107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.426845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.905261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:00.399302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:03.914607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:07.587917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:11.382012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:14.845910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:18.305918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:21.960591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:25.224105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:28.986333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:32.950347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:35.727199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.159951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.364162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.516176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.974030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:00.630903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:04.143406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:07.836482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:11.587409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:15.116161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:18.499943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:22.154938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:25.425658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:29.274518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:33.191130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:35.806531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.240977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.442339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.744032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:41.057119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:00.913687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:04.405817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:08.099706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:11.790735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:15.351913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:18.714620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:22.371438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:25.744739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:29.560906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:33.437889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:35.911202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.325193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.521138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.823391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:41.123996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:01.168914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:04.643573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:08.350937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:12.003311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:15.564433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:18.910980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:22.562356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:25.931947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:29.819617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:33.654401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.003054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.399878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.590861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.906158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:41.203255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:01.434438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:04.894983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:08.619066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:12.281374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:15.782270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:19.179623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:22.848079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:26.127696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:30.088558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:33.884324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.262277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.478010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.666927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:39.988885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:41.270468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:01.651057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:05.127980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:08.844731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:12.529194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:15.986639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:19.640016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:23.116050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:26.302367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:30.351525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:34.093909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.340653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.550375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.739781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.075346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:41.344326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:01.885368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:05.372106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:09.071766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:12.709871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:16.180682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:19.890213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:23.316666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:26.490999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:30.672221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:34.309763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.427733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.620756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.812925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.162758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:41.411541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:02.113361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:05.604442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:09.363131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:12.891973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:16.477462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:20.097238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:23.508971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:26.686935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:30.917240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:34.601892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:36.506931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:37.696880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:38.893257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:32:40.246056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-25T21:32:49.579699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Annual_IncomeBirth_YearCampaign_1Campaign_2Campaign_3Campaign_4Campaign_5Catalog_OrdersComplaint_FlagEdu_LevelFamily_StatusKids_CountLast_VisitPromo_PurchasesSpent_FishSpent_FruitsSpent_GoldSpent_MeatSpent_SweetsSpent_WinesStore_OrdersTeens_CountUser_KeyWeb_OrdersWeb_Visits
Annual_Income1.000-0.1910.1190.1840.4230.4640.1220.7840.0000.0860.1070.426-0.095-0.0930.6020.6120.5550.8050.5610.8570.7770.3440.0760.607-0.627
Birth_Year-0.1911.0000.0000.0000.0000.2050.143-0.2150.1390.0900.1060.2440.068-0.132-0.048-0.074-0.053-0.099-0.032-0.259-0.1870.326-0.043-0.1290.130
Campaign_10.1190.0001.0000.0000.0000.0410.0000.0000.0000.1530.0000.0820.0000.0000.0000.0000.3360.0000.0000.1210.1850.0000.1040.0000.000
Campaign_20.1840.0000.0001.0000.1090.1450.1750.0550.0000.0880.0110.2060.0000.1650.0000.0000.0000.1500.0000.4600.4080.0100.0000.1760.000
Campaign_30.4230.0000.0000.1091.0000.5491.0000.4600.0000.2090.0000.2000.0000.0000.3220.3570.2720.1500.5740.6590.1720.2240.0000.3000.489
Campaign_40.4640.2050.0410.1450.5491.0000.1910.3460.0000.0000.2120.2240.1370.0000.3750.3320.2820.3460.3550.6340.3500.1870.0000.1540.327
Campaign_50.1220.1430.0000.1751.0000.1911.0000.1370.0000.0000.0000.0470.0000.0000.1080.0000.0000.2160.0000.3270.0000.0490.0000.0000.107
Catalog_Orders0.784-0.2150.0000.0550.4600.3460.1371.0000.0000.0000.0810.372-0.1180.0040.6930.6260.6610.8520.6200.8180.6850.1580.1190.594-0.610
Complaint_Flag0.0000.1390.0000.0000.0000.0000.0000.0001.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.000
Edu_Level0.0860.0900.1530.0880.2090.0000.0000.0000.0521.0000.0000.0000.0490.0000.0000.1460.0930.0000.0440.0890.0750.0150.0760.0750.112
Family_Status0.1070.1060.0000.0110.0000.2120.0000.0810.0000.0001.0000.0000.0000.0000.1190.0000.1540.1260.0000.1050.0890.0870.0600.0000.118
Kids_Count0.4260.2440.0820.2060.2000.2240.0470.3720.0000.0000.0001.0000.0700.0570.3530.2730.1950.2980.3030.4000.4140.0000.1050.3030.402
Last_Visit-0.0950.0680.0000.0000.0000.1370.000-0.1180.0000.0490.0000.0701.0000.039-0.0320.0680.011-0.0680.039-0.119-0.0340.137-0.082-0.0620.003
Promo_Purchases-0.093-0.1320.0000.1650.0000.0000.0000.0040.0000.0000.0000.0570.0391.000-0.078-0.1210.1190.006-0.0270.0620.1030.490-0.0160.2070.289
Spent_Fish0.602-0.0480.0000.0000.3220.3750.1080.6930.0000.0000.1190.353-0.032-0.0781.0000.6830.5890.7040.6580.5740.5800.0000.0770.494-0.500
Spent_Fruits0.612-0.0740.0000.0000.3570.3320.0000.6260.0000.1460.0000.2730.068-0.1210.6831.0000.6130.6830.6690.5060.5820.117-0.0530.479-0.475
Spent_Gold0.555-0.0530.3360.0000.2720.2820.0000.6610.0000.0930.1540.1950.0110.1190.5890.6131.0000.6350.5590.5960.5340.307-0.0190.597-0.356
Spent_Meat0.805-0.0990.0000.1500.1500.3460.2160.8520.0000.0000.1260.298-0.0680.0060.7040.6830.6351.0000.6480.8080.7440.2130.0720.683-0.534
Spent_Sweets0.561-0.0320.0000.0000.5740.3550.0000.6200.0000.0440.0000.3030.039-0.0270.6580.6690.5590.6481.0000.4950.5660.1960.0310.471-0.435
Spent_Wines0.857-0.2590.1210.4600.6590.6340.3270.8180.0000.0890.1050.400-0.1190.0620.5740.5060.5960.8080.4951.0000.8130.0000.0840.735-0.453
Store_Orders0.777-0.1870.1850.4080.1720.3500.0000.6850.0000.0750.0890.414-0.0340.1030.5800.5820.5340.7440.5660.8131.0000.1650.0820.619-0.490
Teens_Count0.3440.3260.0000.0100.2240.1870.0490.1580.0000.0150.0870.0000.1370.4900.0000.1170.3070.2130.1960.0000.1651.0000.0000.3090.211
User_Key0.076-0.0430.1040.0000.0000.0000.0000.1190.0330.0760.0600.105-0.082-0.0160.077-0.053-0.0190.0720.0310.0840.0820.0001.0000.062-0.110
Web_Orders0.607-0.1290.0000.1760.3000.1540.0000.5940.0000.0750.0000.303-0.0620.2070.4940.4790.5970.6830.4710.7350.6190.3090.0621.000-0.194
Web_Visits-0.6270.1300.0000.0000.4890.3270.107-0.6100.0000.1120.1180.4020.0030.289-0.500-0.475-0.356-0.534-0.435-0.453-0.4900.211-0.110-0.1941.000

Missing values

2025-07-25T21:32:41.565805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-25T21:32:41.762931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-25T21:32:41.917873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

User_KeyBirth_YearEdu_LevelFamily_StatusAnnual_IncomeKids_CountTeens_CountReg_DateLast_VisitSpent_WinesSpent_FruitsSpent_MeatSpent_FishSpent_SweetsSpent_GoldPromo_PurchasesWeb_OrdersCatalog_OrdersStore_OrdersWeb_VisitsCampaign_1Campaign_2Campaign_3Campaign_4Campaign_5Complaint_FlagContact_CostTotal_Revenue
093701945PhDMarried65846.00017-05-201368562812768040811636400NaN000311
146821958GraduationMarried51876.00015-10-2013889927102284861228100NaN000311
245301948MasterWidow78427.00024-10-201236972195951802613833710300NaN100311
382121971MasterMarried39791.00128-03-2013898515271313212314700NaN000311
464091967GraduationDivorced57136.00018-05-20131826714059934121271757600NaN000311
590581955GraduationWidow79800.00023-09-20126510602153032022415115310NaN100311
642991960GraduationTogether70971.00121-09-2012281001175729312517711115700NaN000311
7104131984GraduationMarried72570.00025-04-2014672748321615114122414612100NaN000311
8189019712n CycleTogether42033.01119-09-20129511142071102700NaN000311
984141962PhDSingle33419.00117-08-2013765601200182204700NaN000311
User_KeyBirth_YearEdu_LevelFamily_StatusAnnual_IncomeKids_CountTeens_CountReg_DateLast_VisitSpent_WinesSpent_FruitsSpent_MeatSpent_FishSpent_SweetsSpent_GoldPromo_PurchasesWeb_OrdersCatalog_OrdersStore_OrdersWeb_VisitsCampaign_1Campaign_2Campaign_3Campaign_4Campaign_5Complaint_FlagContact_CostTotal_Revenue
214310219812n CycleTogether19414.01016-10-201332231235711038000.0000311
21528611983GraduationSingle24072.01016-04-20137991630411028000.0000311
21669181989GraduationMarried28691.0104/7/201356541380411038000.0000311
21779991955PhDTogether75261.00023-04-20131712391741323341715652011.0000311
21845481981GraduationSingle41967.01123-11-20136623410021511034000.0000311
219107851969GraduationMarried44078.01119-06-2014172411020422035000.0000311
22099641979GraduationSingle61825.0017/8/2013561625010055302714284000.0000311
22134121951MasterMarried67381.00115-01-2013678158531107042297010.0000311
22228111963PhDSingle48918.01112/4/201421520900121044000.0000311
22342971969GraduationTogether23228.01026-01-2014711321861022038000.0000311